RT Journal Article
T1 Generalized lattice graphs for 2D-visualization of biological information
A1 González Díaz, Humberto
A1 Peréz Montoto, Lázaro Guillermo
A1 Duardo Sánchez, A.
A1 Paniagua Crespo, Esperanza
A1 Vázquez Prieto, Severo
A1 Vilas, R.
A1 Dea-Ayuela, María Auxiliadora
A1 Bolás-Fernández, Francisco
A1 Munteanu, Cristian Robert
A1 Dorado, Julián
A1 Costas, Javier
A1 Martínez Ubeira, Florencio
K1 Graph theory
K1 Complex networks
K1 Proteomics
K1 Mass spectrometry
K1 Leishmaniosis
K1 2D-electrophoresis
K1 Parasite population polymorphism
K1 Single nucleotide polymorphism
K1 Schizophrenia
K1 Microarray
K1 Cancer
K1 Patents and copyright studies
AB Several graph representations have been introduced for different data in theoretical biology. For instance, complex networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein–protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning in space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. However, despite the proved efficacy of new lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. This work proposes a generalized type of lattice and illustrates how to use it in order to represent and compare biological data from different sources. We exemplify the following cases: protein sequence; mass spectra (MS) of protein peptide mass fingerprints (PMF); molecular dynamic trajectory (MDTs) from structural studies; mRNA microarray data; single nucleotide polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein polymorphisms and protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of Graph theory in theoretical biology and other areas of biomedical sciences.
PB Elsevier
YR 2009
FD 2009
LK https://hdl.handle.net/10347/46675
UL https://hdl.handle.net/10347/46675
LA eng
NO Journal of Theoretical Biology Volume 261, Issue 1, 7 November 2009, Pages 136-147
NO We acknowledge the kind attention and useful comments of the editor and the referees. González-Díaz H., Vilas R. and Munteanu C.R. acknowledge the funding for a research position by Programme Isidro Parga Pondal, Xunta de Galicia. S. Vázquez-Prieto is grateful for the scholarship support from Maria Barbeito Programme, Xunta de Galicia. The authors thank for the partial financial support from project (AGL2006-13936-C01/C02) Ministry of Education and Science, Spain, which is co-financed with European Union funds (FEDER) and for the grants 2007/127 and 2007/144 from the General Directorate of Scientific and Technologic Promotion of the Galician University System of the Xunta de Galicia.
DS Minerva
RD 23 abr 2026